https://publications.eai.eu/index.php/el/issue/feed EAI Endorsed Transactions on e-Learning 2024-07-16T00:00:00+00:00 EAI Publications Department publications@eai.eu Open Journal Systems <p>EAI Endorsed Transactions on e-Learning is open access, a peer-reviewed scholarly journal focused on topics belonging to the variegated and engaging e-Learning landscape, ranging from various types of distance learning (e.g., online, mobile, cloud, hybrid) to virtual laboratory environments supported by sound pedagogies, cutting-edge technologies and much more. The journal publishes research, review, commentaries, editorials, technical articles, and short communications with a triannual frequency. Authors are not charged for article submission and processing.</p> <p><strong>INDEXING</strong>: DOAJ, CrossRef, Google Scholar, ProQuest, EBSCO, CNKI, Dimensions</p> https://publications.eai.eu/index.php/el/article/view/6080 Improvements in Brain Tumor Segmentation Methods Based on Convolutional Neural Networks 2024-05-16T02:58:35+00:00 Yuzhuo Li 15237602561@163.com Lihong Zhang 10460230594@hpu.edu.cn Yingbo Liang liangyingbo519@126.com Chongxin Xu xuchongxin1017@163.com Tong Liu 1934932273@qq.com <p>Convolutional Neural Networks (CNNs) have emerged as a prominent research area in deep learning in recent years. U-Net, an essential model within CNNs, has gradually become a research focus in the field of medical image segmentation due to its remarkable segmentation performance. This paper presents a comprehensive overview of brain tumor segmentation methods based on CNNs. Firstly, it introduces common medical image datasets in the field of brain tumor segmentation. Secondly, it offers detailed reviews on the common improvements to 2D U-Net, 3D U-Net, and improvements based on other CNNs for brain tumor segmentation. Finally, it discusses the future development directions of CNNs for brain tumor segmentation.</p> 2024-12-13T00:00:00+00:00 Copyright (c) 2024 Yuzhuo Li, Lihong Zhang, Yingbo Liang, Chongxin Xu, Tong Liu